Real-time Panorama Composition for Video Surveillance using GPU

Image stitching algorithms combine multiple low resolution images and provide a single high resolution composite image with a larger field of view available for video surveillance. In this research work, we put forward and realize real-time panorama composition for a video surveillance application using the power of a GPU. We utilize a cross platform OpenGL graphics library for real-time online image processing. We parallelize panorama composition using OpenGL objects such as texture object, vertex buffer object and framebuffer object for image warping as well as edge blending to create a seamless panoramic image. We divide our panorama composition algorithm into two stages for image sources with fixed relative positions with each other. Initially in an offline stage, we compute inverse lookup maps and feather weight masks using an OpenCV image processing library for each input image. Subsequently in an online stage, we utilize these inverse lookup maps to generate warped images, which are further edge blended with each other using feather weight masks with the help of OpenGL objects. Our panorama composition is more than 8.5 times faster than the CUDA optimized OpenCV realization. It produces a high resolution seamless panoramic image using nine input image streams each with 800x600 image resolution at about more than 75 frames per second using less than 90MB of GPU memory.